{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "import sklearn\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('iris-data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length_cm</th>\n",
       "      <th>sepal_width_cm</th>\n",
       "      <th>petal_length_cm</th>\n",
       "      <th>petal_width_cm</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length_cm  sepal_width_cm  petal_length_cm  petal_width_cm  \\\n",
       "0              5.1             3.5              1.4             0.2   \n",
       "1              4.9             3.0              1.4             0.2   \n",
       "2              4.7             3.2              1.3             0.2   \n",
       "3              4.6             3.1              1.5             0.2   \n",
       "4              5.0             3.6              1.4             0.2   \n",
       "\n",
       "         class  \n",
       "0  Iris-setosa  \n",
       "1  Iris-setosa  \n",
       "2  Iris-setosa  \n",
       "3  Iris-setosa  \n",
       "4  Iris-setosa  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length_cm</th>\n",
       "      <th>sepal_width_cm</th>\n",
       "      <th>petal_length_cm</th>\n",
       "      <th>petal_width_cm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>145.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.644627</td>\n",
       "      <td>3.054667</td>\n",
       "      <td>3.758667</td>\n",
       "      <td>1.236552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.312781</td>\n",
       "      <td>0.433123</td>\n",
       "      <td>1.764420</td>\n",
       "      <td>0.755058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.055000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.700000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sepal_length_cm  sepal_width_cm  petal_length_cm  petal_width_cm\n",
       "count       150.000000      150.000000       150.000000      145.000000\n",
       "mean          5.644627        3.054667         3.758667        1.236552\n",
       "std           1.312781        0.433123         1.764420        0.755058\n",
       "min           0.055000        2.000000         1.000000        0.100000\n",
       "25%           5.100000        2.800000         1.600000        0.400000\n",
       "50%           5.700000        3.000000         4.350000        1.300000\n",
       "75%           6.400000        3.300000         5.100000        1.800000\n",
       "max           7.900000        4.400000         6.900000        2.500000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150 entries, 0 to 149\n",
      "Data columns (total 5 columns):\n",
      "sepal_length_cm    150 non-null float64\n",
      "sepal_width_cm     150 non-null float64\n",
      "petal_length_cm    150 non-null float64\n",
      "petal_width_cm     145 non-null float64\n",
      "class              150 non-null object\n",
      "dtypes: float64(4), object(1)\n",
      "memory usage: 5.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 145 entries, 0 to 149\n",
      "Data columns (total 5 columns):\n",
      "sepal_length_cm    145 non-null float64\n",
      "sepal_width_cm     145 non-null float64\n",
      "petal_length_cm    145 non-null float64\n",
      "petal_width_cm     145 non-null float64\n",
      "class              145 non-null object\n",
      "dtypes: float64(4), object(1)\n",
      "memory usage: 6.8+ KB\n"
     ]
    }
   ],
   "source": [
    "#Removing all null values row\n",
    "df = df.dropna(subset=['petal_width_cm'])\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1afc795f048>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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UufiXJcpykXnw4/sEG/3AiR+EqalBHK79Gs9c/ixsDgv83iBUq8DB6oNYM3VNdKBnbLcx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\nud2pZtwu/ny1Ysxaky4+HK7s/V+rsVMOjNfKl22OHTyybsCMB8Yr0cBV8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E4F4diNC20O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4arrt0eBRwxjwR/yw1TdAKAps9Q3wawHzbQNHWZyBqp5Z5bv4BzXJ8Z08f00fNd3w/Pw35kOT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\nnHPOZYKfvDjnnHPOOecy4f8BS/75XgqFOlUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1afc795f0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df, hue='class', size=2.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the plots it can be observed that there is some abnormality in the class name. Let's explore further"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Iris-virginica     50\n",
       "Iris-versicolor    45\n",
       "Iris-setosa        44\n",
       "versicolor          5\n",
       "Iris-setossa        1\n",
       "Name: class, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['class'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Two observations can be made from the above results\n",
    "- For 5 data points 'Iris-versicolor' has been specified as 'versicolor' \n",
    "- For 1 data points, 'Iris-setosa' has been specified as 'Iris-setossa'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Iris-virginica     50\n",
       "Iris-versicolor    50\n",
       "Iris-setosa        45\n",
       "Name: class, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['class'].replace([\"Iris-setossa\",\"versicolor\"], [\"Iris-setosa\",\"Iris-versicolor\"], inplace=True)\n",
    "df['class'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simple Logistic Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Consider only two class 'Iris-setosa' and 'Iris-versicolor'. Dropping all other class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "final_df = df[df['class'] != 'Iris-virginica']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length_cm</th>\n",
       "      <th>sepal_width_cm</th>\n",
       "      <th>petal_length_cm</th>\n",
       "      <th>petal_width_cm</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length_cm  sepal_width_cm  petal_length_cm  petal_width_cm  \\\n",
       "0              5.1             3.5              1.4             0.2   \n",
       "1              4.9             3.0              1.4             0.2   \n",
       "2              4.7             3.2              1.3             0.2   \n",
       "3              4.6             3.1              1.5             0.2   \n",
       "4              5.0             3.6              1.4             0.2   \n",
       "\n",
       "         class  \n",
       "0  Iris-setosa  \n",
       "1  Iris-setosa  \n",
       "2  Iris-setosa  \n",
       "3  Iris-setosa  \n",
       "4  Iris-setosa  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Outlier Check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1afc9d13ba8>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IWpDf2cbGVPXzqtqvqsep6hdVdcx6XkS+7ef7EQXeZHq/3LZ99JvF6728GYh2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\nggYvIiIiIiKSC/8fZZJl/9UJzgYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1afc85540b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(final_df, hue='class', size=2.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above plot, sepal_width and sepal_length seems to have outliers. To confirm let's plot them seperately"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SEPAL LENGTH"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000001AFCA702128>]], dtype=object)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WVp6wb9++kXq/w9GjR7Njx45vzR86csdMx1/PheedtS3HmYUTe8R30qPp9Gi6ZenRdv2d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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1afcae83470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "final_df.hist(column = 'sepal_length_cm',bins=20, figsize=(10,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It can be observed from the plot, that for 5 data points values are below 1 and they seem to be outliers. So, these data points\n",
    "are considered to be in 'm' and are converted to 'cm'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\saish\\Anaconda2\\envs\\tensorflow\\lib\\site-packages\\pandas\\core\\indexing.py:517: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self.obj[item] = s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000001AFCA731D68>]], dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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YRkEAAMtkGkemnpfkmilsBwBg6QyFqaq6MMmlSV49nXIAAJbL6JGpX07ygiTfnkItAABL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\nAGEKAGDA/wXJCmgsuhpgCQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1afc88b7940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "final_df.loc[final_df.sepal_length_cm < 1, ['sepal_length_cm']] = final_df['sepal_length_cm']*100\n",
    "final_df.hist(column = 'sepal_length_cm',bins=20, figsize=(10,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SEPAL WIDTH"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "final_df = final_df.drop(final_df[(final_df['class'] == \"Iris-setosa\") & (final_df['sepal_width_cm'] < 2.5)].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1afcaeef2b0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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yiLzEzx9B1PXCmNW+lcfNHbVpy2xwQcxO/b1EndQoDO2mNii98xPAA+823zz2LTHfQEoC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\nj66nwsXFAwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1afcaeef3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(final_df, hue='class', size=2.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Successfully removed outliers!!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Label Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "final_df['class'].replace([\"Iris-setosa\",\"Iris-versicolor\"], [1,0], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length_cm</th>\n",
       "      <th>sepal_width_cm</th>\n",
       "      <th>petal_length_cm</th>\n",
       "      <th>petal_width_cm</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length_cm  sepal_width_cm  petal_length_cm  petal_width_cm  class\n",
       "0              5.1             3.5              1.4             0.2      1\n",
       "1              4.9             3.0              1.4             0.2      1\n",
       "2              4.7             3.2              1.3             0.2      1\n",
       "3              4.6             3.1              1.5             0.2      1\n",
       "4              5.0             3.6              1.4             0.2      1"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Construction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "inp_df = final_df.drop(final_df.columns[[4]], axis=1)\n",
    "out_df = final_df.drop(final_df.columns[[0,1,2,3]], axis=1)\n",
    "#\n",
    "scaler = StandardScaler()\n",
    "inp_df = scaler.fit_transform(inp_df)\n",
    "#\n",
    "X_train, X_test, y_train, y_test = train_test_split(inp_df, out_df, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_tr_arr = X_train\n",
    "X_ts_arr = X_test\n",
    "y_tr_arr = y_train.as_matrix()\n",
    "y_ts_arr = y_test.as_matrix()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input Shape (75, 4)\n",
      "Output Shape (19, 4)\n"
     ]
    }
   ],
   "source": [
    "print('Input Shape', (X_tr_arr.shape))\n",
    "print('Output Shape', X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def weightInitialization(n_features):\n",
    "    w = np.zeros((1,n_features))\n",
    "    b = 0\n",
    "    return w,b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def sigmoid_activation(result):\n",
    "    final_result = 1/(1+np.exp(-result))\n",
    "    return final_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def model_optimize(w, b, X, Y):\n",
    "    m = X.shape[0]\n",
    "    \n",
    "    #Prediction\n",
    "    final_result = sigmoid_activation(np.dot(w,X.T)+b)\n",
    "    Y_T = Y.T\n",
    "    cost = (-1/m)*(np.sum((Y_T*np.log(final_result)) + ((1-Y_T)*(np.log(1-final_result)))))\n",
    "    #\n",
    "    \n",
    "    #Gradient calculation\n",
    "    dw = (1/m)*(np.dot(X.T, (final_result-Y.T).T))\n",
    "    db = (1/m)*(np.sum(final_result-Y.T))\n",
    "    \n",
    "    grads = {\"dw\": dw, \"db\": db}\n",
    "    \n",
    "    return grads, cost\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def model_predict(w, b, X, Y, learning_rate, no_iterations):\n",
    "    costs = []\n",
    "    for i in range(no_iterations):\n",
    "        #\n",
    "        grads, cost = model_optimize(w,b,X,Y)\n",
    "        #\n",
    "        dw = grads[\"dw\"]\n",
    "        db = grads[\"db\"]\n",
    "        #weight update\n",
    "        w = w - (learning_rate * (dw.T))\n",
    "        b = b - (learning_rate * db)\n",
    "        #\n",
    "        \n",
    "        if (i % 100 == 0):\n",
    "            costs.append(cost)\n",
    "            #print(\"Cost after %i iteration is %f\" %(i, cost))\n",
    "    \n",
    "    #final parameters\n",
    "    coeff = {\"w\": w, \"b\": b}\n",
    "    gradient = {\"dw\": dw, \"db\": db}\n",
    "    \n",
    "    return coeff, gradient, costs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def predict(final_pred, m):\n",
    "    y_pred = np.zeros((1,m))\n",
    "    for i in range(final_pred.shape[1]):\n",
    "        if final_pred[0][i] > 0.5:\n",
    "            y_pred[0][i] = 1\n",
    "    return y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Features 4\n",
      "Optimized weights [[-0.13397714  0.13130132 -0.18248682 -0.18319564]]\n",
      "Optimized intercept -0.0241346319213\n",
      "Training Accuracy 1.0\n",
      "Test Accuracy 1.0\n"
     ]
    }
   ],
   "source": [
    "#Get number of features\n",
    "n_features = X_tr_arr.shape[1]\n",
    "print('Number of Features', n_features)\n",
    "w, b = weightInitialization(n_features)\n",
    "#Gradient Descent\n",
    "coeff, gradient, costs = model_predict(w, b, X_tr_arr, y_tr_arr, learning_rate=0.0001,no_iterations=4500)\n",
    "#Final prediction\n",
    "w = coeff[\"w\"]\n",
    "b = coeff[\"b\"]\n",
    "print('Optimized weights', w)\n",
    "print('Optimized intercept',b)\n",
    "#\n",
    "final_train_pred = sigmoid_activation(np.dot(w,X_tr_arr.T)+b)\n",
    "final_test_pred = sigmoid_activation(np.dot(w,X_ts_arr.T)+b)\n",
    "#\n",
    "m_tr =  X_tr_arr.shape[0]\n",
    "m_ts =  X_ts_arr.shape[0]\n",
    "#\n",
    "y_tr_pred = predict(final_train_pred, m_tr)\n",
    "print('Training Accuracy',accuracy_score(y_tr_pred.T, y_tr_arr))\n",
    "#\n",
    "y_ts_pred = predict(final_test_pred, m_ts)\n",
    "print('Test Accuracy',accuracy_score(y_ts_pred.T, y_ts_arr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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P4lVrQdewejk+vLMljWqW5/53Z/HwyLnsydJ5DRHJXVaYekXNyMjwzMyEvWWDrH37eWrM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\nN+Dv7wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1afccd22550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(costs)\n",
    "plt.ylabel('cost')\n",
    "plt.xlabel('iterations (per hundreds)')\n",
    "plt.title('Cost reduction over time')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clf = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\saish\\Anaconda2\\envs\\tensorflow\\lib\\site-packages\\sklearn\\utils\\validation.py:547: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X_tr_arr, y_tr_arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.32958987] [[-0.65118738  1.21001434 -1.38924001 -1.46364162]]\n"
     ]
    }
   ],
   "source": [
    "print (clf.intercept_, clf.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pred = clf.predict(X_ts_arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy from sk-learn: 1.0\n"
     ]
    }
   ],
   "source": [
    "print ('Accuracy from sk-learn: {0}'.format(clf.score(X_ts_arr, y_ts_arr)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
